This study develops and validates a comprehensive predictive road-maintenance framework for intelligent transportation infrastructure in remote areas of Indonesia, integrating the Internet of Things (IoT), machine learning algorithms, and digitalization. A mixed-methods research design was employed across four strategically selected provinces (South Sulawesi, Central Sulawesi, West Papua, and Southwest Papua), integrating multi-source data from Hawkeye vehicle-mounted systems, manual field inspections, motion-sensing cameras, and 10-year historical maintenance records from the Directorate General of Highways. The hybrid machine learning architecture, combining Random Forest and Long Short-Term Memory (LSTM) networks, achieved prediction accuracies ranging from 95.2% (7-day forecasts) to 77.4% (90-day forecasts), with consistently superior performance in Sulawesi provinces compared to the Papua region. The IoT survey demonstrated operational effectiveness, with system uptime ranging from 94.3% in South Sulawesi to 76.2% in Southwest Papua, directly correlating with the maturity of telecommunications infrastructure. Economic analysis revealed cost reductions of 26.4% to 34.2% across provinces, with return on investment periods ranging from 14 to 26 months, demonstrating financial viability despite regional implementation challenges. Statistical analysis confirmed a strong positive correlation (r = 0.912) between baseline infrastructure quality and prediction accuracy, providing empirical validation for targeted investment strategies.